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2019
Chang, B. & Joe, H., 2019. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis, 139, pp.45–63.
Cohen-Freue, G.V. et al., 2019. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics, 13(4), pp.2065-2090. Available at: http://dx.doi.org/10.1214/19-AOAS1269.
Cornish, R. et al., 2019. Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. In International Conference on Machine Learning (ICML). International Conference on Machine Learning (ICML). pp. 1351–1360.
Hadley, D., Joe, H. & Nolde, N., 2019. On the selection of loss severity distributions to model operational risk. Journal of Operational Risk, 14, pp.73-94.
Zhuang, W.W., Hu, B. & Chen, J., 2019. Semiparametric inference for the dominance index under the density ratio model. Biometrika, 106, pp.229–241.
Chen, J. & Liu, Y., 2019. Small area quantile estimation. International Statistical Review, 87, pp.S219–S238.
Chen, Z., Chen, J. & Zhang, Q., 2019. Small area quantile estimation via spline regression and empirical likelihood. Survey Methodology 45-1, 45, pp.81–99.
Joe, H. & Li, H., 2019. Tail densities of skew-elliptical distributions. Journal of Multivariate Analysis, 171, pp.421-435.
Fernandez-Fontelo, A. et al., 2019. Untangling serially dependent underreported count data for gender-based violence. Statistics in Medicine, 38, pp.4404-4422.
Chang, B., Pan, S. & Joe, H., 2019. Vine Copula Structure Learning via Monte Carlo Tree Search. In International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics.
Chang, B., Pan, S. & Joe, H., 2019. Vine copula structure learning via Monte Carlo tree search. In K. Chaudhuri & Sugiyama, M. , eds. 22ND International Conference on Artificial Intelligence and Statistics, Vol 89. 22ND International Conference on Artificial Intelligence and Statistics, Vol 89. pp. 353-361.
2018
Campbell, T. & Broderick, T., 2018. Bayesian coreset construction via greedy iterative geodesic ascent. In International Conference on Machine Learning. International Conference on Machine Learning.
Xia, M. & Gustafson, P., 2018. Bayesian inference for unidirectional misclassification of a binary response trait. Statistics in medicine, 37, pp.933–947.
Wang, W. & Welch, W.J., 2018. Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter Tuning. In Proceedings of AutoML 2018 @ ICML/IJCAI-ECAI. Proceedings of AutoML 2018 @ ICML/IJCAI-ECAI. Available at: https://sites.google.com/site/automl2018icml/accepted-papers/59.pdf.
Kondo, Y. et al., 2018. Bayesian subset selection procedures with an application to lumber strength properties. Sankhya Ser A, p.Accepted Aug 08, 2018.
Bouchard-Côté, A., Vollmer, S.J. & Doucet, A., 2018. The Bouncy Particle Sampler: A non-reversible rejection-free Markov chain Monte Carlo method. Journal of the American Statistical Association, 113, pp.855–867.
Karim, M.Ehsanul et al., 2018. Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies. Statistical methods in medical research, 27, pp.1709–1722.
Campbell, H. & Gustafson, P., 2018. Conditional equivalence testing: An alternative remedy for publication bias. PloS one, 13, p.e0195145.
Nolde, N. & Zhang, J., 2018. Conditional extremes in asymmetric financial markets. Journal of Business & Economic Statistics.
Joe, H., 2018. Dependence properties of conditional distributions of some copula models. Methodology and Computing in Applied Probability, 20, pp.975-1001.
Resende-Casquilho, C., Le, N.D. & Zidek, J.V., 2018. Design of Monitoring Networks using k-Determinantal Point Processes. Environmetrics, 29, p.Accepted Oct 14, 2017.

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